The strong learner \(h\)  is the composition of a feature extractor \(f\)  with a binary classifier \(g\)  implemented by a fully connected layer with sigmoid activation. In this work, \(f\)  is a ResNet18 [1]} pretrained on ImageNet [2]} that encodes an input image of size \(512 \times 512\)  pixels into a vector of size 256, as illustrated in fig:slarchitecture.
